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Performance Investigation of the High Strength Concrete Using Natural Zeolite with Industrial Waste Materials
Concrete is used in the construction of various structural elements. High Strength newlineConcrete (HSC) production for huge infrastructure projects is challenging. The newlinemanufacture of cement significantly causes global carbon dioxide (CO2) emissions. newlineModifications have been made to cement concrete problems to minimize CO2 emissions and Ordinary Portland Cement (OPC) consumption. This research focuses on developing HSC blended with Natural Zeolite (NZ) and industrial by-products like newlineSilica Fume (SF), Fly Ash (FA), and Metakaolin (MK) to enhance concrete quality, newlinesustainability, and performance. Partial replacement of OPC with 5% NZ and industrial waste materials in 5%, 10%, and 15% amounts to produce M60 grade HSC mixes. In the laboratory, 1,200 concrete specimens were tested for mechanical properties for 3, 7, 28, 60, and 90 days, as well as durability tests such as the Rapid Chloride Penetration Test (RCPT) for 28 days and the acid attack test for 60 days. Mix M3 (85% OPC + 5% NZ + 10% MK) exhibited the highest compressive strength at 72 MPa, split tensile strength at 5.3 MPa, and flexural strength at 9.4 MPa for 90 days curing period, attributed to its low porosity. The reactive silica (SiO2) and alumina (Al2O3) in the mix transformed calcium hydroxide (Ca(OH)2) into calcium silicate hydrate (C-S-H) gel and aluminate compounds. This process improved the newlinemicrostructure of the hardened concrete, resulting in increased imperviousness. The newlinestudy also includes the effect of these industrial waste materials on Zeolite concrete by microstructure analysis. The mathematical models were developed using SPSS software to predict the durability and mechanical properties of all the concrete mixes based on the laboratory data, considering parameters like mix proportions and curing days. -
Performance investigation of PID controller in trajectory control of two-link robotic manipulator in medical robots
Robot-assisted surgical procedures have gained much coverage in recent years and favored over manually conducted operations. The medical robots are comprised of manipulators arm that is the multi-degree of freedom positioning devices with a highly non-linear nature to perform various surgical tasks. Due to non-linear effects, robots offer a severe challenge to the control system. Therefore, the control techniques are required for controlling the robots that should be fast enough to accommodate the rapid changes in the system parameters. In this article, the Proportional-Integral-Derivative (PID) controllers performance has been investigated in trajectory control of the Two-Link Robotic Manipulator (TLRM) for reliable functioning of these robots. Tracking error and Control input factors have been used to investigate the PID controllers robustness in trajectory control of TLRM. Eulers-lagrange approach has been used for dynamic analysis of TLRM. This work has been accomplished in the MATLAB/ Simulink environment. 2021 Taru Publications. -
Performance investigation of modular multilevel inverter topologies for photovoltaic applications with minimal switches
Introduction. In recent years, a growing variety of technical applications have necessitated the employment of more powerful equipment. Power electronics and megawatt power levels are required in far too many medium voltage motor drives and utility applications. It is challenging to incorporate a medium voltage grid with only one power semiconductor that has been extensively modified. As a result, in high power and medium voltage settings, multiple power converter structure has been offered as a solution. A multilevel converter has high power ratings while also allowing for the utilization of renewable energy sources. Renewable energy sources such as photovoltaic, wind, and fuel cells may be readily connected to a multilevel inverter topology for enhanced outcomes. The novelty of the proposed work consists of a novel modular inverter structure for solar applications that uses fewer switches. Purpose. The proposed architecture is to decrease the number of switches and Total Harmonic Distortions. There is no need for passive filters, and the proposed design enhances power quality by creating distortion-free sinusoidal output voltage as the level count grows while also lowering power losses. Methods. The proposed topology is implemented with MATLAB / Simulink, using gating pulses and various pulse width modulation methodologies. Moreover, the proposed model also has been validated and compared to the hardware system. Results. Total harmonic distortion, number of power switches, output voltage and number of DC sources are compared with conventional topologies. Practical value. The proposed topology has been very supportive for implementing photovoltaic based multilevel inverter, which is connected to large demand in grid. References 12, table 5, figures 23. E. Parimalasundar, N.M.G. Kumar, P. Geetha, K. Suresh. -
Performance inquisition of web services using soap UI and JMeter
Web Service is a managed code through which the user can expose the existing functionality over the network. Web Service allows multiple applications to communicate with each other. The communication involves passing the data or interaction of two services for a specified action. There are commercial and open source tools available for testing web services. This paper describes about two popular open source tools to test the performance of the web services in terms of response time. The performance is tested based on the time acquired by each service. The comparison study will help in understanding the usage of web service testing tools and adoption of these tools for testing purpose. 2017 IEEE. -
Performance improvement of triple band truncated spiked triangular patch antenna
In this paper, the design of a novel triple band triangular microstrip patch antenna with inset feed is proposed. The triangular patch is designed for a resonant frequency of 2 GHz. The inset feed is placed at a depth of 1/3rd of height from the bottom of the patch for improved return loss. The insertion of two slots and two tabs causes the antenna to resonate at multiple frequencies. The proposed antenna resonates at three frequencies: 1.939 GHz, 2.515 GHz and 3.212 GHz. The truncation of the edges of the patch and the tabs improves the gain and directivity of the antenna. 2016 IEEE. -
Performance Improvement in E-Gun Deposited SiOx- Based RRAM Device by Switching Material Thickness Reduction
A performance improvement by reduction in switching material thickness in a e-gun deposited SiOx based resistive switching memory device was investigated. Reduction in thickness cause thinner filamentary path formation during ON-state by controlling the vacancydefects. Thinner filament cause lowering of operation current from 500 ?A to 100 ?A and also improves the reset current (from >400 ?A to <100 ?A). Switching material thickness reductionalso cause the forming free ability in the device. All these electrical parametric improvements enhance the device reliability performances. The device show >200 dc endurance, >3-hour dataretention and >1000 P/E endurance with 100 ns pulses. 2022 Institute of Physics Publishing. All rights reserved. -
Performance Evaluation of Time-based Recommendation System in Collaborative Filtering Technique
The Collaborative Filtering (CF) technique is the most common neighbourhood-based recommendation strategy, that provides personalized recommendation to a user for the items using a similarity measure. Hence, the selection of the appropriate similarity measure becomes crucial in the CF based recommendation system. The traditional similarity measures merely focus only on the historical ratings provided by the users to compute the similarity, completely ignoring the fact that preferences change over a period of time. Considering this, the paper aims to develop an effective Recommendation System that uses temporal information to capture the changes in the preferences over a period of time. For this, the existing exponential and power time decay functions are integrated with Cosine, Pearson Correlation, and Gower's similarity measures to compute similarity. The similarity is computed at the similarity computation and prediction levels of recommendation processes. Experimental findings in terms of MAE and RMSE on the MovieLens-100k demonstrate that performance of Gower's coefficient is better when applied with the exponential function at the similarity computation level of the recommendation process. 2022 Elsevier B.V.. All rights reserved. -
Performance evaluation of ternary blended geopolymer binders comprising of slag, fly ash and brick kiln rice husk ash
The use of industrial and agro-based precursor materials from local sources can achieve desirable properties for geopolymer binders, and thus realize the carbon-efficient sustainable materials in the construction industry. At the same time, the synergy between these precursors can be assessed using the multilevel material investigation, which has not been explored extensively. Moreover, there are limited studies on ternary geopolymer synthesized with rice husk ash from uncontrolled burning source such as brick kilns. Therefore, this study evaluates the performance of ternary blended geopolymer binders comprised of ground granulated blast furnace slag (GGBFS), fly ash (FLA), and brick kiln rice husk ask (BRHA), implementing the multilevel material approach. The experimental program includes assessment and comparative analyses of the properties of geopolymer binders such as setting time, flow, compressive strength, density, water absorption, and efflorescence. Additionally, X-ray diffraction (XRD) and scanning electron microscopy (SEM) analyses examine crystallographic structure and microscopic morphology of the composite binders. The initial setting time ranged from 21 min to 47 min for ternary mixes, in comparison to 21 min to 58 min for binary mixes. GGBFS significantly contribute in setting of binder due to hydration reaction and formation of C-S-H gel. The flow of ternary mixes exhibits standard deviation of 11.42 mm when compared to 20.96 mm of binary mixes. Lower dispersion in flow values suggests improved coaction between GGBFS, FLA, and BRHA. The compressive strength of ternary mixes improved when compared to the binary mixes. The optimum performance of 60 MPa was obtained for G60A40F95R5, which was 25% and 66.67% higher than binary mixes G60F40 and G60R40, respectively. Similarly, ternary mix G70A30F95R5 showed the least water absorption of 2.08% which was 53% and 58.4% lower than the binary mixes G70F30 and G70R30, respectively. The improvement in the properties of ternary mixes was confirmed from XRD analysis, which reveal coexistence of C-S-H along with crystalline SiO2 that positively improve the microstructure of the composite binder. Moreover, SEM analysis showed dense microstructure for ternary mixes when compared to binary mixes, which further validate the improvement in the strength of such binders. The sustainability analysis discloses the enhanced performance of ternary mixes, wherein, G60A40F95R5 showed 19.35% and 46.23% lower carbon dioxide parameter than binary mixes G60F40 and G60R40, respectively. All in all, the multilevel material investigation provides a great avenue to delve in to the best performing ternary mixes which will find desirable applications in construction industry. 2024 The Authors -
Performance Evaluation of Refractory Bodies Fabricated from Composite Oxide Powders Beneficiated from Black Al-dross
Aluminum Oxide (Corundum, ?-Al2O3) and Magnesium-Aluminum Oxides (Spinel, MgAl2O4) are highly desired refractory materials due to their ability to withstand high-temperature service conditions without corroding and cracking. They are present in composite form in black Aluminum Dross (Al-dross), a hazardous industrial waste. 1 Kg batch of this composite powder was beneficiated from Al-dross to 98+% purity after removing the hazardous Aluminum Nitride (AlN) by aqueous treatment of Al-dross in an environment-friendly manner. The treated slurry was oven dried, ball milled to fine powder, hydraulically pressed, and sintered at 1500 C/6 h into solid cylinders (50 mm diameter 20 mm height). The structural phase analysis of the sintered product (refractory blocks) revealed a highly crystalline XRD pattern with peaks pertaining to only ?-Al2O3 and MgAl2O4. The blocks with Rockwell Hardness values of 4850 HRC, were subjected to thermal shock cycling by following the guidelines of IS 1528 (heat quench between 1000 C and air at ambient) which successfully withstood > 100 shock cycles without failure. SEM was employed to study the fracture surface in an as-sintered state and after thermal shock cycling, to reveal a fine-grained microstructure with clear grain boundaries in the as-sintered state to a glassy matrix with fine cracks at the end of the thermal shock cycle test. The potential for utilization of Al-dross for refractory applications was thus established. 2023 -
Performance evaluation of random forest with feature selection methods in prediction of diabetes
Data mining is nothing but the process of viewing data in different angle and compiling it into appropriate information. Recent improvements in the area of data mining and machine learning have empowered the research in biomedical field to improve the condition of general health care. Since the wrong classification may lead to poor prediction, there is a need to perform the better classification which further improves the prediction rate of the medical datasets. When medical data mining is applied on the medical datasets the important and difficult challenges are the classification and prediction. In this proposed work we evaluate the PIMA Indian Diabtes data set of UCI repository using machine learning algorithm like Random Forest along with feature selection methods such as forward selection and backward elimination based on entropy evaluation method using percentage split as test option. The experiment was conducted using R studio platform and we achieved classification accuracy of 84.1%. From results we can say that Random Forest predicts diabetes better than other techniques with less number of attributes so that one can avoid least important test for identifying diabetes. Copyright 2020 Institute of Advanced Engineering and Science. All rights reserved. -
Performance Evaluation of Predicting IoT Malicious Nodes Using Machine Learning Classification Algorithms
The prediction of malicious nodes in Internet of Things (IoT) networks is crucial for enhancing network security. Malicious nodes can significantly impact network performance across various scenarios. Machine learning (ML) classification algorithms provide binary outcomes ("yes" or "no") to accurately identify these nodes. This study implements various classifier algorithms to address the problem of malicious node classification, using the SensorNetGuard dataset. The dataset, comprising 10,000 records with 21 features, was preprocessed and used to train multiple ML models, including Logistic Regression, Decision Tree, Naive Bayes, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). Performance evaluation of these models followed the ML workflow, utilizing Python libraries such as scikit-learn, Seaborn, Matplotlib, and Pandas. The results indicated that the Naive Bayes classifier outperformed others with an accuracy of 98.1%. This paper demonstrates the effectiveness of ML classifiers in detecting malicious nodes in IoT networks, providing a robust predictive model for real-time application. The SensorNetGuard dataset is available on the IEEE data port and Kaggle platform. 2024, Prof.Dr. ?skender AKKURT. All rights reserved. -
Performance evaluation of parallel genetic algorithm for brain MRI segmentation in hadoop and spark /
Indian Journal of Science and Technology, Vol.8, Issue 48, pp.1-7, ISSN: 0974-6846 (Print), 0974-5645 (Online). -
Performance Evaluation of OTFS Under Different Channel Conditions for LEO Satellite Downlink
Orthogonal Time Frequency Space (OTFS) modulation scheme is being actively pursued as a viable alternative to Orthogonal Frequency Division Multiplexing (OFDM) modulation scheme in future wireless standards due to the inherent ability of OTFS to mitigate the Doppler effects in high mobility scenarios. The inclusion of Non Terrestrial Network (NTN) in Release 17 of 3GPP (3rd generation partnership project) New Radio (NR) standard signifies the vital role of Satellite Communications to achieve coverage extension, capability augmentation and seamless global connectivity. In this context, it becomes important to study the suitability of OTFS modulation scheme with respect to satellite channel scenarios. In this paper, we consider the downlink channel scenarios defined by 3GPP NR NTN for Low Earth Orbit (LEO) satellites at sub-6 GHz and millimetre wave frequencies for evaluating the performance of OTFS modulation schemes. Simulation results using LMMSE (Linear Minimum Mean Square Error) and MRC (Maximum Ratio Combining) detection algorithms confirm that OTFS modulation is highly robust against Doppler effects and performs consistently across all channel conditions. From simulation results, it is observed that the performance of iterative MRC detection is better than LMMSE for 16QAM and 64QAM modulation schemes by achieving respective gains of around 5 dB and 10 dB for corresponding Bit Error Rate (BER) values of 0.01 and 0.1. 2023 IEEE. -
Performance evaluation of Map-reduce jar pig hive and spark with machine learning using big data
Big data is the biggest challenges as we need huge processing power system and good algorithms to make a decision. We need Hadoop environment with pig hive, machine learning and hadoopecosystem components. The data comes from industries. Many devices around us and sensor, and from social media sites. According to McKinsey There will be a shortage of 15000000 big data professionals by the end of 2020. There are lots of technologies to solve the problem of big data Storage and processing. Such technologies are Apache Hadoop, Apache Spark, Apache Kafka, and many more. Here we analyse the processing speed for the 4GB data on cloudx lab with Hadoop mapreduce with varing mappers and reducers and with pig script and Hive querries and spark environment along with machine learning technology and from the results we can say that machine learning with Hadoop will enhance the processing performance along with with spark, and also we can say that spark is better than Hadoop mapreduce pig and hive, spark with hive and machine learning will be the best performance enhanced compared with pig and hive, Hadoop mapreduce jar. Copyright 2020 Institute of Advanced Engineering and Science. All rights reserved. -
Performance evaluation of machinelearning techniques indiabetes prediction
Diabetes diagnosis is very important at preliminary stage rather than treatment. In todays world devices like sensors are used for detection of diabetes. Accurate classification techniques are required for automatic identification of diabetes disease. In regards to research diabetes prediction with minimal number of attributes (test parameters) is to be identified earlier research states about feature reduction but with less predictive accuracy. In this regards, this work exploits machine learning techniques(methodology) such as Logistic Regression (LR), Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF) and Neural Network (NN) with 10-fold Cross Validation (CV) for classification and prediction of diabetes with Feature Selection Methods (FSMs) using R platform. Above all models enable us to investigate the relationship between a categorical outcome and a set of explanatory variables. The experiment was conducted on PIMA Indian diabetes dataset selected from UCI machine learning repository. From the experimental results it is identified that for full set of diabetes dataset attributes, Classification Accuracy (CA) achieved was 84.25%whereas with reduced set attributes an accuracy of 85.24% is achieved using NN with 10-fold CV technique compared to others which will help in medical application to predict diabetes with minimal features. BEIESP. -
Performance evaluation of diesel engine using genetic algorithm
?Abstract: Engine analysis and optimization is not a new approach to the field of automobiles. It has always been a keen focus in the research of experts domestically as well as internationally, the control of Air-Fuel Ratio (AFR) in transient operating conditions of engine. For the last few decades, the industry and economic expansion of developed countries has showed a clean increase in the vehicle production as well as transport volume. Global warming, acid rain, greenhouse effect and air pollution problems related to emission of CO2, NOx, PM, CO and unburned HC, together with the consumption of fossil fuels, unite to create serious problems at a global level. Therefore it is a research study considering all these current issues and taking it to a new level of optimization for the output of a better efficiency, better economy and less pollution. Performance of Diesel Engine is evaluated by parameters like Power, Torque and Specific Fuel Consumption. 2018, Blue Eyes Intelligence Engineering and Sciences Publication. All rights reserved. -
Performance Evaluation of Convolutional Neural Networks for Stellar Image Classification: A Comparative Study
This study analyzes three distinct convolutional neural network (CNN) models, ResNet, Parallel CNN, and VGG16, for object classification using the Star-Galaxy Classification dataset. The dataset comprises a vast collection of celestial object images, including galaxies, stars, and quasars. The effectiveness of each CNN model is evaluated based on accuracy, a commonly used performance metric. The results reveal that the Parallel CNN model achieved the highest accuracy of 90.08% in classifying celestial objects, followed by VGG16 with an accuracy of 86%, and ResNet with an accuracy of 83%. Specifically, the Parallel CNN model demonstrates superior performance in classifying galaxies and stars. These findings provide valuable insights into the strengths and weaknesses of each model for this specific classification task, guiding the development of more effective CNN models for similar applications in cosmology and other fields. This research contributes to the growing literature on CNN models' application in astronomy and underscores the importance of selecting appropriate models to achieve high accuracy in object classification tasks. The study's insights can be utilized to inform the development of more effective CNN models for similar tasks and facilitate advancements in astronomical research. 2023 IEEE. -
Performance evaluation of artificial neural networks in sustainable modelling biodiesel synthesis
Biodiesel is a characteristic and inexhaustible homegrown fuel removed from creature fats or vegetable oil and liquor through a transesterification response. The exploration work means to assess the exhibition of biodiesel blend. In this paper, biodiesel was displayed and improved by utilizing a hereditary calculation and Artificial Neural Network (ANN). In AI, hereditary calculations and counterfeit neural organizations assume a significant part in displaying biodiesel blend. To upgrade an excellent arrangement hereditary calculation was created. The mix of ANN and Genetic Algorithm gives the ideal condition as the temperature of methanol molar proportion, impetus fixation. It tentatively decides the exhibition trademark like the Coefficient of determination and Absolute Average deviation (AAD). It predicts the Fatty Acid Methyl Ester (FAME) model productively than Response Surface Methodology (RSM). The exhibition examination is reenacted and hypothetical outcomes are recorded then it is contrasted with constant information to decide the exactness of ANN. 2022 Elsevier Ltd -
Performance Evaluation of Area-Based Segmentation Technique on Ambient Sensor Data for Smart Home Assisted Living
Activity recognition(AR) is a popular subject of research in the recent past. Recognition of activities performed by human beings, enables the addressing of challenges posed by many real-world applications such as health monitoring, providing security etc. Segmentation plays a vital role in AR. This paper evaluates the efficiency of Area-Based Segmentation using different performance measures. Area-Based segmentation was proposed in our earlier research work. The evaluation of the Area-Based segmentation technique is conducted on four real world datasets viz. Aruba17, Shib010, HH102, and HH113 comprising of data pertaining to an individual, living in the test bed home. Machine learning classifiers, SVM-R, SVM-P, NB and KNN are adopted to validate the performance of Area-Based segmentation. Amongst the four chosen classification algorithms SVM-R exhibits better in all the four datasets. Area-Based segmentation recognise the four test bed activities with accuracies of 0.74, 0.98, 0.66, and 0.99 respectively. The results reveal that Area based segmentation can efficiently segment sensor data stream which aids in accurate recognition of smart home activities. 2019 Procedia Computer Science. All rights reserved. -
Performance Evaluation Frameworks in the Context of Indian Microfinance Institutions
The paper conducts a detailed examination of the existing evaluative frameworks for microfinance institutions to gauge the differences and similarities. Efficiency evaluates how MFIs are meeting the performance standards considering time and budget constraints. Outreach evaluates the effectiveness of MFIs in reaching the beneficiaries. Relative efficiency scores were calculated using data envelopment analysis and outreach was measured in five different dimensions (pentagon model). Further, cluster analysis assisted in categorizing the MFIs into five value clusters. The study compares both outreach performance and relative efficiency scores employing ANOVA and correlation analysis. The study was conducted among the Indian context when the sector was hit by crisis during 2010. Paper brought out important insights about the sample. Indian MFIs were found to be more socially efficient, since the social dimension taken into consideration was number of female clients and majority of Indian MFIs has exclusive female focus. The correlation tests found that relative efficiency scores are positively related to depth (poor focus) and length (sustainability) outreach. The results showed that cluster analysis model basing outreach scores was more comprehensive and captured more information compared to the data envelopment model relative efficiency scores. The study is original in its approach in using cluster analysis for outreach performance and in the objective of comparing the two different models. 2019 Aruna Balammal et al., published by Sciendo 2019.